Fast Iterative Shrinkage-Thresholding Algorithm with Continuation for Brain Injury Monitoring Imaging Based on Electrical Impedance Tomography

Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However, brain injury monitoring by EIT imaging suffers from image noise (IN) and resolution problems, causing blurred reconstructions. To address the...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 22; no. 24; p. 9934
Main Authors: Liu, Xuechao, Zhang, Tao, Ye, Jian’an, Tian, Xiang, Zhang, Weirui, Yang, Bin, Dai, Meng, Xu, Canhua, Fu, Feng
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 01.12.2022
MDPI
Subjects:
Algorithms
Brain
Brain Injuries - diagnostic imaging
brain injury monitoring imaging
Comparative analysis
Electric Impedance
electrical impedance tomography
fast iterative shrinkage-thresholding algorithm
Humans
Image Processing, Computer-Assisted - methods
Injuries
Inverse problems
Ischemia
least absolute shrinkage and selection operator model
Methods
Numerical analysis
Patient monitoring equipment
Phantoms, Imaging
reconstruction algorithm
Simulation methods
Tomography
Tomography - methods
Tomography, X-Ray Computed
Traumatic brain injury
reconstruction algorithm
least absolute shrinkage and selection operator model
fast iterative shrinkage-thresholding algorithm
electrical impedance tomography
brain injury monitoring imaging
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Electrical impedance tomography (EIT) is low-cost and noninvasive and has the potential for real-time imaging and bedside monitoring of brain injury. However, brain injury monitoring by EIT imaging suffers from image noise (IN) and resolution problems, causing blurred reconstructions. To address these problems, a least absolute shrinkage and selection operator model is built, and a fast iterative shrinkage-thresholding algorithm with continuation (FISTA-C) is proposed. Results of numerical simulations and head phantom experiments indicate that FISTA-C reduces IN by 63.2%, 47.2%, and 29.9% and 54.4%, 44.7%, and 22.7%, respectively, when compared with the damped least-squares algorithm, the split Bergman, and the FISTA algorithms. When the signal-to-noise ratio of the measurements is 80–50 dB, FISTA-C can reduce IN by 83.3%, 72.3%, and 68.7% on average when compared with the three algorithms, respectively. Both simulation and phantom experiments suggest that FISTA-C produces the best image resolution and can identify the two closest targets. Moreover, FISTA-C is more practical for clinical application because it does not require excessive parameter adjustments. This technology can provide better reconstruction performance and significantly outperforms the traditional algorithms in terms of IN and resolution and is expected to offer a general algorithm for brain injury monitoring imaging via EIT.
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22249934